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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enhancements and Deployment of the TDAQ System for the Mu2e Experiment

The Real Time Processing Systems Division at Fermilab has deployed new features to the Off-The-Shelf Data Acquisition framework (otsdaq) for the Mu2e experiment. The Mu2e experiment will search for the coherent neutrino-less conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement of 10,000 times over existing limits. Such a charged lepton flavor-violating reaction probes new physics at a scale unavailable at present or planned high-energy colliders. The Mu2e Trigger and Data Acquisition (TDAQ) system uses otsdaq as its online Data Acquisition System (DAQ) framework. otsdaq integrates the artdaq and art frameworks for event transfer, filtering, and processing. otsdaq is a web-based DAQ software suite focusing on flexibility and scalability and provides a multi-user interface accessible through a web browser. artdaq handles the entire data stream, which is read over the peripheral component interconnect express (PCIe) bus to a software filter algorithm that selects events combined with the data flux coming from a cosmic-ray veto (CRV) system. Detector front-ends are configured through the PCIe bus by customized otsdaq plugins. The otsdaq slow controls infrastructure has been further developed using the experimental physics and industrial control system (EPICS) open-source platform for monitoring, controlling, alarming, and archiving. The detector control system (DCS) for Mu2e has been integrated into otsdaq. The production TDAQ and DCS system has been deployed at the experimental hall and is being debugged and optimized for experiment operations. We report on the feature enhancements and deployment of otsdaq for Mu2e.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
DOE OSTI2022

Integrating Spatial and Ethnographic Methods for Resilience Research: A Thick Mapping Approach for Hurricane Maria in Puerto Rico

Hurricane Maria left unprecedented impacts on Puerto Rican communities, leaving some without infrastructure services and unable to communicate with family for several months. Here, to understand the forms of community-level resilience that emerged while hard infrastructure systems took time recover, this article (1) abductively explores resilience as an emergent phenomenon of complex adaptive systems; (2) identifies subsequent forms of social capital, local adaptive capacities, and manifestations of quantifiable variables, such as infrastructure performance, in community experiences; and (3) demonstrates a framework to integrate disparate methodologies for resilience assessments via a multiplicity of mappings of space and place. We combine ethnographic and geospatial methods into an interactive GeoApp for analysis using participant-coded narratives and a series of geospatial indicators as a thick map. Thick mapping facilitates quantitative and qualitative data analysis at several scales, while enabling qualitative query of collected narratives. Results highlight local innovation, community bonding and bridging, and nuances in the role of public institutions as emergent elements of resilience. The thick map shows how top-down assessments can be augmented by thick data and how multiple framings can be anchored in the same system or place. These findings are important to inform and integrate community-oriented and technocentric solutions toward resilience-enhancing measures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Integrated Data Analysis to Improve Te and Zeff Measurement on NSTX-U

The multi-energy soft x-ray (ME-SXR) camera is a new concept for a fusion plasma diagnostic based on a pixelated detector with adjustable photon cutoff thresholds. It is a single, compact, solid-state detector which combines spatial and spectral sensitivity and can be configured remotely at run time, making it ideal for a fusion environment. The detector can be configured for application to a wide range of physics problems including impurity transport, electron temperature profiles, and non-thermal effects. The detector has been installed on the Madison Symmetric Torus (MST) as part of a collaboration between Princeton Plasma Physics Laboratory and the University of Wisconsin–Madison to develop the technology and analysis techniques. A full diagnostic forward model of the ME-SXR diagnostic has been developed to enable analysis of the raw photon count data. This model incorporates the atomic physics underlying the plasma emission, diagnostic geometry, and detector response characteristics. Employing this model, a Bayesian analysis approach is used to extract electron temperature from the raw data. This analysis approach uses all available information, clearly quantifies prior information, and handles uncertainties transparently. The ME-SXR camera is being used to study the 3D (helical, stellarator-like) state that forms in the MST device. This helical core structure has improved energy confinement. The transition to the helical state is accompanied by a rise in electron temperature in the central part of the helical structure, but then this enhancement fluctuates. The characteristics of this fluctuation are being compared to a magneto-hydrodynamics model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

On the effectiveness of neural operators at zero-shot weather downscaling

Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

17 WIND ENERGY↗
DOE OSTI2020

Dalitz analysis of $D^0 → K^–π^+η$ decays at Belle

We present the results of the first Dalitz plot analysis of the decay D 0 → K – π + η . The analysis is performed on a data set corresponding to an integrated luminosity of 953 fb – 1 collected by the Belle detector at the asymmetric-energy e + e – KEKB collider. The Dalitz plot is well described by a combination of the six resonant decay channels K ¯ * ( 892 ) 0 η , K – a 0 ( 980 ) + , K – a 2 ( 1320 ) + , K ¯ * ( 1410 ) 0 η , K * ( 1680 ) – π + and K 2 * ( 1980 ) – π + , together with K π and K η S-wave components. The decays K * ( 1680 ) – → K – η and K 2 * ( 1980 ) – → K – η are observed for the first time. We measure ratio of the branching fractions, B ( D 0 → K – π + η ) B ( D 0 → K – π + ) = 0.500 ± 0.002 ( stat ) ± 0.020 ( syst ) ± 0.003 ( B PDG ) . Using the Dalitz fit result, the ratio B ( K * ( 1680 ) → K η ) B ( K * ( 1680 ) → K π ) is measured to be 0.11 ± 0.02 ( stat ) – 0.04 + 0.06 ( syst ) ± 0.04 ( B PDG ) ; this is much lower than the theoretical expectations ( ≈ 1 ) made under the assumption that K * ( 1680 ) is a pure 1 3 D 1 state. The product branching fraction B ( D 0 → [ K 2 * ( 1980 ) – → K – η ] π + ) = ( 2. 2 – 1.9 + 1.7 ) × 10 – 4 is determined. In addition, the π η ' contribution to the a 0 ( 980 ) ± resonance shape is confirmed with 10.1 σ statistical significance using the three-channel Flatté model. We also measure B ( D 0 → K ¯ * ( 892 ) 0 η ) = ( 1.4 1 – 0.12 + 0.13 ) % . This is consistent with, and more precise than, the current world average ( 1.02 ± 0.30 ) % , deviates with a significance of more than 3 σ from the theoretical predictions of (0.51–0.92)%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Batteries Included: Top 10 Findings from Berkeley Lab Research on the Growth of Hybrid Power Plants in the United States

One of the most important electric power system trends of the 2010s was the rapid deployment of wind turbines and photovoltaic arrays, but a twist for the 2020s may be the rapid deployment of ‘hybrid’ generation resources. Hybrid power plants typically combine solar or wind (or other energy sources) with co-located storage. While hybridization helps to ease the challenge of balancing variable supply and demand, its relative novelty means that research is needed to facilitate integration and promote innovation. Combining the characteristics of multiple energy, storage, and conversion technologies poses complex questions for grid operations and economics. Project developers, system operators, planners, and regulators would benefit from better data, methods, and tools to estimate the costs, values, and system impacts of hybrid projects. This publication showcases some of Berkeley Lab’s robust research program intended to support private- and public-sector decision-making about hybrid plants in the United States. Our short briefing summarizes articles that we published between 2020 and 2022, links to the in-depth reports, and provides contact details for further engagement on the specific research topics: Growth: Developer interest in hybrid power plants is strong and growing Price vs. Value: PV+storage hybrids have low PPA prices and high value in some regions Market Drivers: Solar hybridization is driven by tax credits and other benefits Configuration Choices: Market prices have incentivized shorter duration batteries with PV Capacity Value: The capacity contribution of a hybrid is less than the sum of its parts Ancillary Services: AS markets are a valuable yet fleeting option for hybrids Market Participation: Hybrids can more flexibly engage with electricity markets Operations: The power system value of hybrids depends on how they are operated Distributed Hybrids: Growth of customer-sited PV+storage hybrids offers new opportunities Future Research: Where next? Priority areas for hybrid power research.

25 ENERGY STORAGE↗
DOE OSTI2025

Integration of renewable energy generation and storage systems for emissions reduction in an islanded campus microgrid

Microgrid connected building communities are projected to play an integral part in the clean energy transition. These types of systems, when integrated with distributed energy resources (DERs) such as combined heat and power (CHP), district heating and cooling, renewable generation, and energy storage, can provide clean, reliable power to critical facilities and vulnerable communities. The intermittent nature of renewable generation is a challenge when integrating renewables into any grid system, but particularly in islanded microgrids. The University of Texas at Austin (UT) operates an islanded microgrid powered by a CHP plant, while also utilizing district heating and cooling systems and thermal energy storage (TES). High fidelity operating data was used to develop a validated reduced order model of UT’s integrated campus energy systems to serve as a testbed for use in a case study. Hypothetical renewable energy installations on land owned by UT in west Texas were modeled and integrated into the validated campus models along with battery energy storage (BES). Simulations showed that a combination of renewable energy from wind, and optimally controlled 24-hour thermal and battery storage systems could reduce carbon dioxide emissions on campus by 45.4%. The additional retrofit of burner systems to utilize hydrogen natural gas blends resulted in an overall annual emissions reduction of 54.7%. Carbon capture and storage eliminated the majority of the remaining emissions with increased plant energy expenditure. The presented simulations display the practical limitations of a CHP system complemented by renewable generation and short-term storage in eliminating emissions. Furthermore, results highlight the need for further research and development in long duration storage technologies and hydrogen fueled turbines to increase penetration of renewable energy and reduce emissions.

CHP↗
DOE OSTI2025

Toward a Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry‐Informed Transfer Learning

Optimally designing applications of molten salts requires knowledge of their thermophysical properties over a wide range of temperatures and compositions. There exist significant gaps in existing databases and this data can be challenging to experimentally measure due to high temperatures, salt corrosivity, and salt hygroscopicity. Existing databases have been used to create Redlich–Kister (RK) models for mixture density showing improved accuracy with respect to ideal mixing assumptions, but these models require subcomponent data measurements for each new system, therefore lacking generality. In order to address generalizability and data sparsity, a transfer learning procedure is proposed to train deep neural networks (DNNs) using a combination of semi‐empirical relationships (RK), data from the thermophysical arm of the molten salt thermal properties database and universal ab initio properties of component mixtures taken from the joint automated repository for various integrated simulations (JARVIS) classical force‐field inspired descriptors database to predict density in molten salts. Herein, it is shown that DNNs predict molten salt density with an r 2 over 0.99 and a mean absolute percentage error under 1%, outperforming alternative methods.

inorganic materials↗
DOE OSTI2021

Integrating renewable energy into mining operations: Opportunities, challenges, and enabling approaches

Mining is one of the most energy-intensive industries worldwide. It also provides a critical source of raw materials for the manufacturing, transportation, construction, and energy sectors. Demand for raw materials is projected to increase as the world population grows and many low-income economies become middle-income countries. This growth in mineral demand, coupled with falling mineral ore grade, will likely increase the mining industry’s energy demand, used for activities across exploration, extraction, beneficiation and processing, and refining. At the time of this writing, mine operations are – due to their remoteness – dependent on fossil fuels such as diesel, heavy oils, and coal. In principle, mining could use energy recovery, renewable energy, and carbon capture to supplement, replace, or mitigate the impacts of fossil fuel use. However, a combination of renewable-energy technologies would be required. We explore challenges, opportunities, and enabling approaches to integrate renewable energy technologies into mining operations by examining the literature, including academic work, technical reports, and data produced by international agencies. Here, we find that despite numerous opportunities, technical issues still need to be considered, but solutions can tailor renewables to the mining industry. Further research should focus on identifying specific opportunities, technologies, and implementation strategies across the value chain of a variety of minerals with similar operational procedures.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Validation of two-layer model for underexpanded hydrogen jets

Previous studies have shown that the two-layer model more accurately predicts hydrogen dispersion than the conventional notional nozzle models without significantly increasing the computational expense. However, the model was only validated for predicting the concentration distribution and has not been adequately validated for predicting the velocity distributions. In the present study, particle imaging velocimetry (PIV) was used to measure the velocity field of an underexpanded hydrogen jet released at 10 bar from a 1.5 mm diameter orifice. The two-layer model was the used to calculate the inlet conditions for a two-dimensional axisymmetric CFD model to simulate the hydrogen jet downstream of the Mach disk. The predicted velocity spreading and centerline decay rates agreed well with the PIV measurements. The predicted concentration distribution was consistent with data from previous planar Rayleigh scattering measurements used to verify the concentration distribution predictions in an earlier study. The jet spreading was also simulated using several widely used notional nozzle models combined with the integral plume model for comparison. These results show that the velocity and concentration distributions are both better predicted by the two-layer model than the notional nozzle models to complement previous studies verifying only the predicted concentration profiles. Thus, this study shows that the two-layer model can accurately predict the jet velocity distributions as well as the concentration distributions as verified earlier. Though more validation studies are needed to improve confidence in the model and increase the range of validity, the present work indicates that the two-layer model is a promising tool for fast, accurate predictions of the flow fields of underexpanded hydrogen jets.

08 HYDROGEN↗
DOE OSTI2023

Deep learning enables nanoscale X-ray 3D imaging with limited data

Deep neural network can greatly improve tomography reconstruction with limited data. A recent effort of combining ptycho-tomography model with the 3D U-net demonstrated a significant reduction in both the number of projections and computation time, and showed its potential for integrated circuit imaging that requires high-resolution and fast measurement speed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Testing biasedness of self-reported microbusiness innovation in the annual business survey

This study tests for potential bias in self-reported innovation due to the inclusion of a research and development (R&D) module that only microbusinesses (less than 10 employees) receive in the Annual Business Survey (ABS). Previous research found that respondents to combined innovation/R&D surveys reported innovation at lower rates than respondents to innovation-only surveys. A regression discontinuity design is used to test whether microbusinesses, which constitute a significant portion of U.S. firms with employees, are less likely to report innovation compared to other small businesses. In the vicinity of the 10-employee threshold, the study does not detect statistically significant biases for new-to-market and new-to-business product innovation. Statistical power analysis confirms the nonexistence of biases with a high power. Comparing the survey design of ABS to earlier combined innovation/R&D surveys provides valuable insights for the proposed integration of multiple Federal surveys into a single enterprise platform survey. The findings also have important implications for the accuracy and reliability of innovation data used as an input to policymaking and business development strategies in the United States.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Multi-Objective Boundary Analysis of Discrete and Integrated SiC FET Modular Non-inverting Buck and Boost Converters for Fuel Cell EVs

This paper presents a multi-objective analysis of discrete and integrated SiC FET-based non-inverting buck-boost converter modules for modular fuel cell electric vehicle (EV) systems. Two converter ratings, 60 kW and 90 kW, are evaluated for both implementations, scalable up to 420 kW and 450 kW, respectively. Performance is assessed across efficiency, volumetric and gravimetric power density, cost, thermal stress, and estimated lifetime, where lifetime is derived from SiC FET B10 power-cycling data and junction temperature variations at rated power. A normalized overall performance index combined with a Pareto-boundary framework is used to identify configurations that optimally balance competing objectives. Results show that most configurations lie on the Pareto front, providing balanced trade-offs, while certain high-power discrete (90 kW at 450 kW) and integrated (60 kW at 180−420 kW) configurations are dominated. In general, discrete modules are more favorable for lower-power modular systems due to higher power density and lower cost, whereas integrated modules become more advantageous at higher power levels due to improved thermal behavior and longer lifetime. These findings provide practical design guidance for scalable fuel cell converter architectures and highlight the importance of system-level trade-offs in modular power electronics design.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2020

Aircraft-Integrated Meteorological Measurement System (AIMMS) Instrument Handbook

The AIMMS-20 probe is manufactured by Aventech Research Inc. and measures platform position, velocity, attitude, ambient temperature, static pressure, and the differential pressures from a 5-port hemispheric gust probe, and combines all these measured data into a calculation of the ambient winds. A detailed description of the AIMMS-20 specifications is available here. Note: The Aventech engineers use the aircraft-centric frame of reference common to aeronautical engineering, which defines z as down.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An automated multiplexed turbidometric and data collection system for measuring growth kinetics of anaerobes dependent on gaseous substrates

Standard methods of monitoring the growth kinetics of anaerobic microorganisms are generally impractical when there is a protracted or indeterminate period of active growth, and when high numbers of samples or replications are required. As part of our studies of the adaptive evolution of a simple anaerobic syntrophic mutualism, requiring the characterization of many isolates and alternative syntrophic pairings, here we developed a multiplexed growth monitoring system using a combination of commercially available electronics and custom designed circuitry and materials. This system automatically monitors up to 64 sealed, and as needed pressurized, culture tubes and reports the growth data in real-time through integration with a customized relational database. The utility of this system was demonstrated by resolving minor differences in growth kinetics associated with the adaptive evolution of a simple microbial community comprised of a sulfate reducing bacterium, Desulfovibrio vulgaris, grown in syntrophic association with Methanococcus maripaludis, a hydrogenotrophic methanogen.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Forest Soil Carbon Efflux Evaluation Across China: A New Estimate With Machine Learning

Forest soil respiration (Rs) plays an important role in the carbon balance of terrestrial ecosystems. China's forest occupies a large part of the world's forest. However, due to the lack of integrated observation data and appropriate upscaling methodologies, substantial uncertainties exist in the Rs evaluation, which limits our understanding of the carbon balance. Here, we re-evaluated the total soil carbon effluxes across China by combining field observations from 634 published annual Rs with a machine learning technique (i.e., Random Forest (RF)). Our results revealed that the combination of systematic measurements with the RF model allowed a definite estimate. The average annual Rs was 773.6 g C m -2 yr -1 , ranging from 404.6 to 1,772.3 g C m -2 yr -1 . Total forest soil carbon effluxes amounted to 1.17 Pg C yr -1 in China. Geographically, annual Rs showed a clear spatially increasing trend from northeast to southwest. Forest type is an important factor in determining the soil respiration rate. Bamboo and Evergreen broadleaf forests were higher than other types of forests. In conclusion, these results provide a unique insight into the magnitudes and mechanisms of soil CO 2 emissions in China's forest ecosystems.

58 GEOSCIENCES↗